如何使用缺失值执行RMSE? [英] How to perform RMSE with missing values?

问题描述

我有一个庞大的数据集，其中包含679行和16列，缺少30％的缺失值.因此，我决定使用来自impute软件包的impute.knn函数来估算这些缺失值，然后得到了一个数据集，其中包含679行16列，但是没有缺失值.

I have a huge dataset with 679 rows and 16 columns with 30 % of missing values. So I decided to impute this missing values with the function impute.knn from the package impute and I got a dataset with 679 rows and 16 columns but without the missing values.

但是现在我想使用RMSE检查准确性，我尝试了2种选择:

But now I want to check the accuracy using the RMSE and I tried 2 options:

1. 加载软件包hydroGOF并应用rmse函数
2. sqrt(mean (obs-sim)^2), na.rm=TRUE)

1. load the package hydroGOF and apply the rmse function
2. sqrt(mean (obs-sim)^2), na.rm=TRUE)

在两种情况下，我会出现错误:errors in sim .obs: non numeric argument to binary operator.

In two situations I have the error: errors in sim .obs: non numeric argument to binary operator.

之所以发生这种情况，是因为原始数据集包含一个NA值(某些值缺失).

This is happening because the original data set contains an NA value (some values are missing).

如果删除缺失值，如何计算RMSE?然后obs和sim将具有不同的大小.

How can I calculate the RMSE if I remove the missing values? Then obs and sim will have different sizes.

推荐答案

简单...

sqrt( sum( (df$model - df$measure)^2 , na.rm = TRUE ) / nrow(df) )

显然，假设您的数据框称为df，并且您必须确定 N (即nrow(df)包括两行缺少数据的行；是否要排除 N 观察中的这些?我想是的，所以您可能想使用sum( !is.na(df$measure) )来代替nrow(df))，或者紧跟在@Joshua之后

Obviously assuming your dataframe is called df and you have to decide on your N ( i.e. nrow(df) includes the two rows with missing data; do you want to exclude these from N observations? I'd guess yes, so instead of nrow(df) you probably want to use sum( !is.na(df$measure) ) ) or, following @Joshua just

sqrt( mean( (df$model-df$measure)^2 , na.rm = TRUE ) )

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